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作者：Dharamshi, A.; Neufeld, A.; Gao, L. L.; Bien, J.; Witten, D.
作者单位：University of Washington; University of Washington Seattle; Williams College; University of British Columbia; University of Southern California; University of Washington; University of Washington Seattle
摘要：Common workflows in machine learning and statistics rely on the ability to partition the information in a dataset into independent portions. Recent work has shown that this may be possible even when conventional sample splitting is not, such as when the number of samples, $ n $, is one or when observations are not independent and identically distributed. In the case of multivariate Gaussian data, these alternatives to sample splitting require knowledge of the covariance matrix. In many importa...
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作者：McClean, A.; Branson, Z.; Kennedy, E. H.
作者单位：Carnegie Mellon University
摘要：In causal inference, sensitivity models are used to assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter (which quantifies the degree of unmeasured confounding) is often difficult to interpret. For this reason, researchers sometimes compare the sensitivity parameter to an estimate of measured confounding, a process known as calibration or benchmarking. However, calibrated estimates are not always interpreted correctly, and uncertainty in the estimate of ...
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作者：Rybak, J.; Battey, H. S.; Bharath, K.
作者单位：Imperial College London; University of Nottingham
摘要：That parameterization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level sparsity after reparameterization. In covariance models, positive definiteness enforces additional constraints on how sparsity can legitimately manifest. It is therefore natural to consider reparameterization maps in which sparsity respects positive definiteness. This paper provides insight into structures on the phy...
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作者：Wan, P.
作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
摘要：In this paper, we characterize the extremal dependence of $ d $ asymptotically dependent variables using a class of random vectors on the $ (d-1) $-dimensional hyperplane perpendicular to the diagonal vector $ \mathbf{1}=(1,\ldots,1) $. This translates analyses of multivariate extremes to analyses on a linear vector space, opening up possibilities for applying existing statistical techniques based on linear operations. As an example, we demonstrate how to obtain lower-dimensional approximation...
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作者：Aronow, P. M.; Chang, Haoge; Lopatto, Patrick
作者单位：Yale University; Columbia University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson-Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment effect when the treatment effe...
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作者：Fong, E.; Yiu, A.
作者单位：University of Hong Kong; University of Southampton
摘要：The martingale posterior framework replaces the elicitation of the likelihood and prior with that of a sequence of one-step-ahead predictive densities for Bayesian inference. Posterior sampling then involves the imputation of unobserved quantities and can then be carried out in an expedient and parallelizable manner using predictive resampling, without requiring Markov chain Monte Carlo. Recent work has investigated the use of plug-in parametric predictive densities, combined with stochastic g...
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作者：Kent, A.; Berrett, T. B.; Yu, Y.
作者单位：University of Warwick
摘要：We consider the problem of two-sample testing under a local differential privacy constraint using a permutation procedure. We develop testing procedures that are optimal up to logarithmic factors for general discrete distributions and continuous distributions subject to a smoothness constraint. Both noninteractive and interactive tests are considered, and we show that allowing interactivity results in an improvement in the minimax separation rates. Our results show that permutation procedures ...
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作者：Ghosh, Aditya; Deb, Nabarun; Karmakar, Bikram; Sen, Bodhisattva
作者单位：Stanford University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
摘要：Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum's estimator is robust against outliers with a breakdown point that un...
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作者：Koo, Taehyeon; Pashley, Nicole E.
作者单位：Columbia University; Rutgers University System; Rutgers University New Brunswick
摘要：Researchers often turn to block randomization to increase the precision of their inference or for practical reasons, such as in multi-site trials. However, if the number of treatments under consideration is large, it may not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-population, design-based framework for natural alternatives to the complete block design that do not require reducing the number of treatment arms, name...
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作者：Durmus, Alain; Gruffaz, Samuel; Hasenpflug, Mareike; Rudolf, Daniel
作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Universite Paris Saclay; Universite Paris Cite; University of Passau
摘要：We propose a theoretically justified and practically applicable slice-sampling-based Markov chain Monte Carlo method for approximate sampling from probability measures on Riemannian manifolds. The latter naturally arise as posterior distributions in Bayesian inference of matrix-valued parameters, for example belonging to either the Stiefel or the Grassmann manifold. Our method, called geodesic slice sampling, generalizes hit-and-run slice sampling on $ \mathbb{R}<^>{d} $ to Riemannian manifold...
